






'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TF_MODEL_FOR_PRETRAINING_MAPPING,
						    TFMobileBertForMaskedLM,
						    TFMobileBertForMultipleChoice,
						    TFMobileBertForNextSentencePrediction,
						    TFMobileBertForPreTraining,
						    TFMobileBertForQuestionAnswering,
						    TFMobileBertForSequenceClassification,
						    TFMobileBertForTokenClassification,
						    TFMobileBertModel,
						)


@require_tf
class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					(
					    (
					        TFMobileBertModel,
					        TFMobileBertForMaskedLM,
					        TFMobileBertForNextSentencePrediction,
					        TFMobileBertForPreTraining,
					        TFMobileBertForQuestionAnswering,
					        TFMobileBertForSequenceClassification,
					        TFMobileBertForTokenClassification,
					        TFMobileBertForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					a_        =					(
					    {
					        "feature-extraction": TFMobileBertModel,
					        "fill-mask": TFMobileBertForMaskedLM,
					        "question-answering": TFMobileBertForQuestionAnswering,
					        "text-classification": TFMobileBertForSequenceClassification,
					        "token-classification": TFMobileBertForTokenClassification,
					        "zero-shot": TFMobileBertForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					a_        =					False
					a_        =					False




					def 		A      (  self  :		List[str]				, _A  :		int				, _A  :		Optional[int]				, _A  :		Optional[Any]=False      )    ->							Dict:
								UpperCAmelCase_      :       Any          =       super()._prepare_for_class(_A				, _A				, return_labels=_A      )

								if return_labels:
											if model_class in get_values(_A      ):
														UpperCAmelCase_      :       List[Any]          =       tf.zeros(self.model_tester.batch_size				, dtype=tf.intaa      )

								return inputs_dict


					class  snake_case__      (					UpperCamelCase):




										def __init__(  self  :		int				, _A  :		Dict				, _A  :		int=13				, _A  :		Optional[Any]=7				, _A  :		int=True				, _A  :		Union[str, Any]=True				, _A  :		Tuple=True				, _A  :		Optional[Any]=True				, _A  :		Optional[int]=99				, _A  :		List[Any]=32				, _A  :		Optional[int]=32				, _A  :		Optional[int]=2				, _A  :		Optional[Any]=4				, _A  :		Union[str, Any]=37				, _A  :		Optional[Any]="gelu"				, _A  :		Tuple=0.1				, _A  :		int=0.1				, _A  :		Any=5_12				, _A  :		Tuple=16				, _A  :		Dict=2				, _A  :		List[str]=0.02				, _A  :		Dict=3				, _A  :		Dict=4				, _A  :		Union[str, Any]=None				, )    ->							Tuple:
													UpperCAmelCase_      :       str          =       parent
													UpperCAmelCase_      :       Any          =       batch_size
													UpperCAmelCase_      :       Dict          =       seq_length
													UpperCAmelCase_      :       Any          =       is_training
													UpperCAmelCase_      :       Dict          =       use_input_mask
													UpperCAmelCase_      :       int          =       use_token_type_ids
													UpperCAmelCase_      :       Union[str, Any]          =       use_labels
													UpperCAmelCase_      :       Any          =       vocab_size
													UpperCAmelCase_      :       Dict          =       hidden_size
													UpperCAmelCase_      :       int          =       num_hidden_layers
													UpperCAmelCase_      :       Optional[Any]          =       num_attention_heads
													UpperCAmelCase_      :       List[Any]          =       intermediate_size
													UpperCAmelCase_      :       Optional[int]          =       hidden_act
													UpperCAmelCase_      :       Optional[Any]          =       hidden_dropout_prob
													UpperCAmelCase_      :       str          =       attention_probs_dropout_prob
													UpperCAmelCase_      :       Dict          =       max_position_embeddings
													UpperCAmelCase_      :       List[Any]          =       type_vocab_size
													UpperCAmelCase_      :       Any          =       type_sequence_label_size
													UpperCAmelCase_      :       Tuple          =       initializer_range
													UpperCAmelCase_      :       Tuple          =       num_labels
													UpperCAmelCase_      :       List[str]          =       num_choices
													UpperCAmelCase_      :       Tuple          =       scope
													UpperCAmelCase_      :       int          =       embedding_size




										def 		A      (  self  :		Optional[Any]      )    ->							int:
													UpperCAmelCase_      :       Tuple          =       ids_tensor([self.batch_size, self.seq_length]				, self.vocab_size      )

													UpperCAmelCase_      :       Tuple          =       None
													if self.use_input_mask:
																UpperCAmelCase_      :       List[Any]          =       random_attention_mask([self.batch_size, self.seq_length]      )

													UpperCAmelCase_      :       Any          =       None
													if self.use_token_type_ids:
																UpperCAmelCase_      :       Optional[int]          =       ids_tensor([self.batch_size, self.seq_length]				, self.type_vocab_size      )

													UpperCAmelCase_      :       Optional[int]          =       None
													UpperCAmelCase_      :       List[str]          =       None
													UpperCAmelCase_      :       int          =       None
													if self.use_labels:
																UpperCAmelCase_      :       List[str]          =       ids_tensor([self.batch_size]				, self.type_sequence_label_size      )
																UpperCAmelCase_      :       Tuple          =       ids_tensor([self.batch_size, self.seq_length]				, self.num_labels      )
																UpperCAmelCase_      :       Optional[int]          =       ids_tensor([self.batch_size]				, self.num_choices      )

													UpperCAmelCase_      :       Union[str, Any]          =       MobileBertConfig(
													    vocab_size=self.vocab_size				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, max_position_embeddings=self.max_position_embeddings				, type_vocab_size=self.type_vocab_size				, initializer_range=self.initializer_range				, embedding_size=self.embedding_size				, )

													return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




										def 		A      (  self  :		Optional[Any]				, _A  :		List[Any]				, _A  :		List[Any]				, _A  :		Tuple				, _A  :		Optional[int]				, _A  :		str				, _A  :		Optional[int]				, _A  :		int      )    ->							Optional[Any]:
													UpperCAmelCase_      :       Tuple          =       TFMobileBertModel(config=_A      )
													UpperCAmelCase_      :       Dict          =       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													UpperCAmelCase_      :       Tuple          =       model(_A      )

													UpperCAmelCase_      :       Tuple          =       [input_ids, input_mask]
													UpperCAmelCase_      :       Tuple          =       model(_A      )

													UpperCAmelCase_      :       Optional[Any]          =       model(_A      )

													self.parent.assertEqual(
													    result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)      )
													self.parent.assertEqual(result.pooler_output.shape				, (self.batch_size, self.hidden_size)      )




										def 		A      (  self  :		int				, _A  :		Dict				, _A  :		List[str]				, _A  :		Tuple				, _A  :		int				, _A  :		List[Any]				, _A  :		Any				, _A  :		str      )    ->							Dict:
													UpperCAmelCase_      :       Tuple          =       TFMobileBertForMaskedLM(config=_A      )
													UpperCAmelCase_      :       Dict          =       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													UpperCAmelCase_      :       Tuple          =       model(_A      )
													self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.vocab_size)      )




										def 		A      (  self  :		Tuple				, _A  :		List[Any]				, _A  :		Optional[Any]				, _A  :		Dict				, _A  :		Dict				, _A  :		List[str]				, _A  :		List[Any]				, _A  :		int      )    ->							Any:
													UpperCAmelCase_      :       Tuple          =       TFMobileBertForNextSentencePrediction(config=_A      )
													UpperCAmelCase_      :       Optional[int]          =       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													UpperCAmelCase_      :       int          =       model(_A      )
													self.parent.assertEqual(result.logits.shape				, (self.batch_size, 2)      )




										def 		A      (  self  :		Any				, _A  :		Tuple				, _A  :		Any				, _A  :		Any				, _A  :		Union[str, Any]				, _A  :		List[Any]				, _A  :		int				, _A  :		Tuple      )    ->							Union[str, Any]:
													UpperCAmelCase_      :       List[Any]          =       TFMobileBertForPreTraining(config=_A      )
													UpperCAmelCase_      :       List[Any]          =       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													UpperCAmelCase_      :       Optional[Any]          =       model(_A      )
													self.parent.assertEqual(
													    result.prediction_logits.shape				, (self.batch_size, self.seq_length, self.vocab_size)      )
													self.parent.assertEqual(result.seq_relationship_logits.shape				, (self.batch_size, 2)      )




										def 		A      (  self  :		Dict				, _A  :		int				, _A  :		Tuple				, _A  :		Dict				, _A  :		Tuple				, _A  :		List[Any]				, _A  :		Optional[int]				, _A  :		Tuple      )    ->							Dict:
													UpperCAmelCase_      :       Optional[int]          =       self.num_labels
													UpperCAmelCase_      :       Union[str, Any]          =       TFMobileBertForSequenceClassification(config=_A      )
													UpperCAmelCase_      :       List[str]          =       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													UpperCAmelCase_      :       Dict          =       model(_A      )
													self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)      )




										def 		A      (  self  :		Dict				, _A  :		Dict				, _A  :		Union[str, Any]				, _A  :		Dict				, _A  :		Any				, _A  :		Optional[Any]				, _A  :		Union[str, Any]				, _A  :		Optional[int]      )    ->							int:
													UpperCAmelCase_      :       int          =       self.num_choices
													UpperCAmelCase_      :       Dict          =       TFMobileBertForMultipleChoice(config=_A      )
													UpperCAmelCase_      :       str          =       tf.tile(tf.expand_dims(_A				, 1      )				, (1, self.num_choices, 1)      )
													UpperCAmelCase_      :       Any          =       tf.tile(tf.expand_dims(_A				, 1      )				, (1, self.num_choices, 1)      )
													UpperCAmelCase_      :       Optional[int]          =       tf.tile(tf.expand_dims(_A				, 1      )				, (1, self.num_choices, 1)      )
													UpperCAmelCase_      :       List[Any]          =       {
													    '''input_ids''': multiple_choice_inputs_ids,
													    '''attention_mask''': multiple_choice_input_mask,
													    '''token_type_ids''': multiple_choice_token_type_ids,
													}
													UpperCAmelCase_      :       int          =       model(_A      )
													self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_choices)      )




										def 		A      (  self  :		Optional[Any]				, _A  :		Optional[Any]				, _A  :		Union[str, Any]				, _A  :		Optional[int]				, _A  :		Dict				, _A  :		Any				, _A  :		Tuple				, _A  :		Union[str, Any]      )    ->							Union[str, Any]:
													UpperCAmelCase_      :       str          =       self.num_labels
													UpperCAmelCase_      :       Union[str, Any]          =       TFMobileBertForTokenClassification(config=_A      )
													UpperCAmelCase_      :       str          =       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													UpperCAmelCase_      :       List[str]          =       model(_A      )
													self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.num_labels)      )




										def 		A      (  self  :		str				, _A  :		Dict				, _A  :		Optional[Any]				, _A  :		Dict				, _A  :		Optional[int]				, _A  :		Any				, _A  :		List[str]				, _A  :		int      )    ->							str:
													UpperCAmelCase_      :       Tuple          =       TFMobileBertForQuestionAnswering(config=_A      )
													UpperCAmelCase_      :       Any          =       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
													UpperCAmelCase_      :       Any          =       model(_A      )
													self.parent.assertEqual(result.start_logits.shape				, (self.batch_size, self.seq_length)      )
													self.parent.assertEqual(result.end_logits.shape				, (self.batch_size, self.seq_length)      )





										def 		A      (  self  :		Union[str, Any]      )    ->							Dict:
													UpperCAmelCase_      :       Any          =       self.prepare_config_and_inputs()
													(
													    (
													    UpperCAmelCase_
													)    ,      (
													    UpperCAmelCase_
													)    ,      (
													    UpperCAmelCase_
													)    ,      (
													    UpperCAmelCase_
													)    ,      (
													    UpperCAmelCase_
													)    ,      (
													    UpperCAmelCase_
													)    ,      (
													    UpperCAmelCase_
													)    ,      
													)      :       Union[str, Any]          =       config_and_inputs
													UpperCAmelCase_      :       Any          =       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
													return config, inputs_dict






					def 		A      (  self  :		List[Any]      )    ->							Tuple:
								UpperCAmelCase_      :       str          =       TFMobileBertModelTest.TFMobileBertModelTester(self      )
								UpperCAmelCase_      :       List[Any]          =       ConfigTester(self				, config_class=_A				, hidden_size=37      )




					def 		A      (  self  :		List[Any]      )    ->							Optional[Any]:
								self.config_tester.run_common_tests()




					def 		A      (  self  :		List[Any]      )    ->							List[Any]:
								UpperCAmelCase_      :       Optional[int]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*_A      )




					def 		A      (  self  :		Tuple      )    ->							str:
								UpperCAmelCase_      :       Any          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*_A      )




					def 		A      (  self  :		Dict      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[int]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_A      )




					def 		A      (  self  :		List[Any]      )    ->							int:
								UpperCAmelCase_      :       Dict          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_A      )




					def 		A      (  self  :		Union[str, Any]      )    ->							Dict:
								UpperCAmelCase_      :       Tuple          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*_A      )




					def 		A      (  self  :		int      )    ->							List[str]:
								UpperCAmelCase_      :       Dict          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*_A      )




					def 		A      (  self  :		List[str]      )    ->							List[str]:
								UpperCAmelCase_      :       Dict          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_A      )




					def 		A      (  self  :		Dict      )    ->							int:
								UpperCAmelCase_      :       Tuple          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*_A      )





					@slow
					def 		A      (  self  :		Dict      )    ->							str:
								# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								for model_name in ["google/mobilebert-uncased"]:
											UpperCAmelCase_      :       Optional[int]          =       TFMobileBertModel.from_pretrained(_A      )
											self.assertIsNotNone(_A      )






@require_tf
class  snake_case__      (					unittest.TestCase):




					@slow
					def 		A      (  self  :		str      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Union[str, Any]          =       TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased'''      )
								UpperCAmelCase_      :       List[str]          =       tf.constant([[0, 1, 2, 3, 4, 5]]      )
								UpperCAmelCase_      :       Dict          =       model(_A      )[0]

								UpperCAmelCase_      :       List[str]          =       [1, 6, 3_05_22]
								self.assertEqual(output.shape				, _A      )

								UpperCAmelCase_      :       List[Any]          =       tf.constant(
								    [
								        [
								            [-4.5_919_547, -9.248_295, -9.645_256],
								            [-6.7_306_175, -6.440_284, -6.6_052_837],
								            [-7.2_743_506, -6.7_847_915, -6.024_673],
								        ]
								    ]      )
								tf.debugging.assert_near(output[:, :3, :3]				, _A				, atol=1e-4      )










'''simple docstring'''





from __future__ import annotations

import os
from collections.abc import Mapping

_UpperCamelCase						:						Any  =					tuple[int, int]


class  snake_case__      :




					def __init__(  self  :		List[str]				, _A  :		set[int]				, _A  :		Mapping[EdgeT, int]      )    ->							None:
								UpperCAmelCase_      :       set[int]          =       vertices
								UpperCAmelCase_      :       dict[EdgeT, int]          =       {
								    (min(_A      ), max(_A      )): weight for edge, weight in edges.items()
								}




					def 		A      (  self  :		Union[str, Any]				, _A  :		EdgeT				, _A  :		int      )    ->							None:
								self.vertices.add(edge[0]      )
								self.vertices.add(edge[1]      )
								UpperCAmelCase_      :       List[str]          =       weight





					def 		A      (  self  :		str      )    ->							Graph:
								UpperCAmelCase_      :       Graph          =       Graph({min(self.vertices      )}				, {}      )
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int
								UpperCAmelCase_      :       EdgeT
								UpperCAmelCase_      :       int

								while len(subgraph.vertices      ) < len(self.vertices      ):
											UpperCAmelCase_      :       Optional[Any]          =       max(self.edges.values()      ) + 1
											for edge, weight in self.edges.items():
														if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
																	if weight < min_weight:
																				UpperCAmelCase_      :       Dict          =       edge
																				UpperCAmelCase_      :       Tuple          =       weight

											subgraph.add_edge(_A				, _A      )

								return subgraph






def __UpperCAmelCase   (					A					:			str = "p107_network.txt"		)    ->   int:
			UpperCAmelCase_      :       str          =       os.path.abspath(os.path.dirname(A		)		)
			UpperCAmelCase_      :       str          =       os.path.join(A			,       A		)
			UpperCAmelCase_      :       dict[EdgeT, int]          =       {}
			UpperCAmelCase_      :       list[str]
			UpperCAmelCase_      :       int
			UpperCAmelCase_      :       int

			with open(A		) as f:
						UpperCAmelCase_      :       int          =       f.read().strip().split('''\n'''		)

			UpperCAmelCase_      :       Any          =       [line.split(''','''		) for line in data]

			for edgea in range(1			,       len(A		)		):
						for edgea in range(A		):
									if adjaceny_matrix[edgea][edgea] != "-":
												UpperCAmelCase_      :       Optional[int]          =       int(adjaceny_matrix[edgea][edgea]		)

			UpperCAmelCase_      :       Graph          =       Graph(set(range(len(A		)		)		)			,       A		)

			UpperCAmelCase_      :       Graph          =       graph.prims_algorithm()

			UpperCAmelCase_      :       int          =       sum(graph.edges.values()		)
			UpperCAmelCase_      :       int          =       sum(subgraph.edges.values()		)

			return initial_total - optimal_total


if __name__ == "__main__":
						print(f'''{solution() = }''')


